code: def test_rl(time, strftime, sagemaker_session, coach_mxnet_version):
      rl = RLEstimator(
          entry_point=SCRIPT_PATH,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          toolkit=RLToolkit.COACH,
          toolkit_version=coach_mxnet_version,
          framework=RLFramework.MXNET,
      )
  
      inputs = "s3://mybucket/train"
  
      rl.fit(inputs=inputs, experiment_config=EXPERIMENT_CONFIG)
  
      sagemaker_call_names = [
          c[0] for c in sagemaker_session.method_calls
      ]
      assert sagemaker_call_names == ["train", "logs_for_job"]
      boto_call_names = [
          c[0] for c in sagemaker_session.boto_session.method_calls
      ]
      assert boto_call_names == ["resource"]
  
      expected_train_args = _create_train_job(
          RLToolkit.COACH.value,
          coach_mxnet_version,
          RLFramework.MXNET.value,
      )
      expected_train_args["input_config"][0]["DataSource"][
          "S3DataSource"
      ]["S3Uri"] = inputs
      expected_train_args["experiment_config"] = EXPERIMENT_CONFIG
  
      actual_train_args = sagemaker_session.method_calls[0][2]
      assert actual_train_args == expected_train_args
  
      model = rl.create_model()
      supported_versions = TOOLKIT_FRAMEWORK_VERSION_MAP[
          RLToolkit.COACH.value
      ]
      framework_version = supported_versions[coach_mxnet_version][
          RLFramework.MXNET.value
      ]
  
      expected_image_base = "520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet:{}-gpu-py3"
      submit_dir = "s3://notmybucket/sagemaker-rl-mxnet-{}/source/sourcedir.tar.gz".format(
          TIMESTAMP
      )
      assert {
          "Environment": {
              "SAGEMAKER_SUBMIT_DIRECTORY": submit_dir,
              "SAGEMAKER_PROGRAM": "dummy_script.py",
              "SAGEMAKER_REGION": "us-west-2",
              "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
          },
          "Image": expected_image_base.format(framework_version),
          "ModelDataUrl": "s3://m/m.tar.gz",
      } == model.prepare_container_def(GPU)
  
      assert "cpu" in model.prepare_container_def(CPU)["Image"]
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker.mxnet import MXNetModel, MXNetPredictor
  from sagemaker.rl import RLEstimator, RLFramework, RLToolkit, TOOLKIT_FRAMEWORK_VERSION_MAP
  from sagemaker.session_settings import SessionSettings
  from sagemaker.tensorflow import TensorFlowModel, TensorFlowPredictor
label_desc: Use image node_id 9 for calling a global function with example usage: test_rl(time, strftime, sagemaker_session, coach_mxnet_version) without return types